Prediction of university enrollment using computational intelligence

This work presents a study on prediction of university enrollment using three computational intelligence (CI) techniques. The enrollment forecasting has been considered as a form of time series prediction using CI techniques that include an artificial neural network (ANN), a neuro-fuzzy inference system (ANFIS) and an aggregated fuzzy time series model. A novel form of ANN, namely, single multiplicative neuron (SMN), as an alternative to traditional multi-layer perceptron (MLP), has been used for time series prediction. A variation of population based heuristic optimization approach, namely, co-operative particle swarm optimization (COPSO), has been used to estimate the parameters for the SMN, the combination is termed here as COPSO-SMN. The second CI technique used for time series prediction is adaptive neuro fuzzy inference system (ANFIS) which combines the advantages of ANN and fuzzy logic (FL). The third technique is based on an aggregated fuzzy time series model that utilizes both global trend of the past data and the local fuzzy fluctuations. The first two CI models have been developed for one-step-ahead prediction of time series using the data of the current time and three previous time steps. The models based on these three techniques have been trained using a previously published dataset. The models have been further trained and tested using enrollment data of Georgia Southern University for the period of 1924-2012. The training and test performances of all three CI techniques have been compared for the datasets.